Skip to main content

Advertisement

SpringerLink
Log in

Artificial intelligence: a critical review of current applications in pancreatic imaging

  • Invited Review
  • Published:
Japanese Journal of Radiology Aims and scope Submit manuscript

A Correction to this article was published on 10 March 2021

This article has been updated

Abstract

The applications of artificial intelligence (AI), including machine learning and deep learning, in the field of pancreatic disease imaging are rapidly expanding. AI can be used for the detection of pancreatic ductal adenocarcinoma and other pancreatic tumors but also for pancreatic lesion characterization. In this review, the basic of radiomics, recent developments and current results of AI in the field of pancreatic tumors are presented. Limitations and future perspectives of AI are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Change history

Abbreviations

3D:

Three dimensional

AI:

Artificial intelligence

AIP:

Autoimmune pancreatitis

ANN:

Artificial neural network

AUC:

Area under receiver operating characteristic curve

CAD:

Computer-aided diagnosis

CCN:

Convolutional neural networks

CI:

Confidence interval

CT:

Computed tomography

DDLS:

Discriminative dictionary learning for segmentation

DL:

Deep learning

DSC:

Dice similarity coefficient

IPMN:

Intraductal papillary mucinous neoplasm

MCN:

Mucinous cystic neoplasms

ML:

Machine learning

MRI:

Magnetic resonance imaging

PDAC:

Pancreatic ductal adenocarcinoma

RF:

Random forests

SCN:

Serous cystic neoplasms

SPEN:

Solid pseudopapillary epithelial neoplasms

SVM:

Support vector machines

VOI:

Volume of interest

References

  1. Park S, Chu LC, Hruban RH, Vogelstein B, Kinzler KW, Yuille AL, et al. Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. Diagn Interv Imaging. 2020;101(9):555–64.

    Article  CAS  PubMed  Google Scholar 

  2. Weisberg EM, Chu LC, Park S, Yuille AL, Kinzler KW, Vogelstein B, et al. Deep lessons learned: radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis. Diagn Interv Imaging. 2020;101(2):111–5.

    Article  CAS  PubMed  Google Scholar 

  3. Azoulay A, Cros J, Vullierme MP, de Mestier L, Couvelard A, Hentic O, et al. Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma. Diagn Interv Imaging. 2020;101(12):821–30.

    Article  CAS  PubMed  Google Scholar 

  4. Nakaura T, Higaki T, Awai K, Ikeda O, Yamashita Y. A primer for understanding radiology articles about machine learning and deep learning. Diagn Interv Imaging. 2020;101(12):765–70.

    Article  PubMed  Google Scholar 

  5. Gao X, Wang X. Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: a preliminary study. Diagn Interv Imaging. 2020;101(2):91–100.

    Article  CAS  PubMed  Google Scholar 

  6. Nakata N. Recent technical development of artificial intelligence for diagnostic medical imaging. Jpn J Radiol. 2019;37:103–8.

    Article  PubMed  Google Scholar 

  7. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biol. 1990;52:99–115.

    Article  CAS  PubMed  Google Scholar 

  8. Simpson AL, Antonelli M, Bakas S, et al (2019) A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv; published online Feb 25. http://arxiv.org/ abs/1902.09063.

  9. Watson MD, Baimas-George MR, Murphy KJ, Pickens RC, Iannitti DA, Martinie JB, et al. Pure and hybrid deep learning models can predict pathologic tumor response to neoadjuvant therapy in pancreatic adenocarcinoma: a pilot study. Am Surg. 2020. https://doi.org/10.1177/0003134820982557.

    Article  PubMed  Google Scholar 

  10. van der Pol CB, Tang A. Imaging database preparation for machine learning. Can Assoc Radiol J. 2020. https://doi.org/10.1177/0846537120967720.

    Article  PubMed  Google Scholar 

  11. Rastegar S, Vaziri M, Qasempour Y, Akhash MR, Abdalvand N, Shiri I, et al. Radiomics for classification of bone mineral loss: a machine learning study. Diagn Interv Imaging. 2020;101(9):599–610.

    Article  CAS  PubMed  Google Scholar 

  12. Roca P, Attye A, Colas L, Tucholka A, Rubini P, Cackowski S, et al. Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI. Diagn Interv Imaging. 2020;101(12):795–802.

    Article  CAS  PubMed  Google Scholar 

  13. Couteaux V, Si-Mohamed S, Renard-Penna R, Nempont O, Lefevre T, Popoff A, et al. Kidney cortex segmentation in 2D CT with U-Nets ensemble aggregation. Diagn Interv Imaging. 2019;100(4):211–7.

    Article  CAS  PubMed  Google Scholar 

  14. Schmauch B, Herent P, Jehanno P, Dehaene O, Saillard C, Aubé C, Luciani A, Lassau N, Jégou S. Diagnosis of focal liver lesions from ultrasound using deep learning. Diagn Interv Imaging. 2019;100(4):227–33.

    Article  CAS  PubMed  Google Scholar 

  15. Park S, Chu LC, Fishman EK, Yuille AL, Vogelstein B, Kinzler KW, et al. Annotated normal CT data of the abdomen for deep learning: challenges and strategies for implementation. Diagn Interv Imaging. 2020;101(1):35–44.

    Article  CAS  PubMed  Google Scholar 

  16. Thomassin-Naggara I, Balleyguier C, Ceugnart L, Heid P, Lenczner G, Maire A, et al. Conseil national professionnel de la radiologie et imagerie médicale (G4). Artificial intelligence and breast screening: French radiology community position paper. Diagn Interv Imagin. 2019;100(10):553–66.

    Article  CAS  Google Scholar 

  17. Yang Z, Zhang L, Zhang M, Feng J, Wu Z, Ren F, et al. Pancreas segmentation in abdominal CT scans using inter-/intra-slice contextual information with a cascade neural network. Conf Proc IEEE Eng Med Biol Soc. 2019;2019:5937–40.

    Google Scholar 

  18. Lassau N, Bousaid I, Chouzenoux E, Lamarque JP, Charmettant B, Azoulay M, et al. Three artificial intelligence data challenges based on CT and MRI. Diagn Interv Imaging. 2020;101(12):783–8.

    Article  CAS  PubMed  Google Scholar 

  19. Kumar H, DeSouza SV, Petrov MS. Automated pancreas segmentation from computed tomography and magnetic resonance images: a systematic review. Comput Methods Programs Biomed. 2019;178:319–28.

    Article  PubMed  Google Scholar 

  20. Oliveira B, Queiros S, Morais P, Torres HR, Gomes-Fonseca J, Fonseca JC, et al. A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multiorgan segmentation from computed tomography. Med Image Anal. 2018;45:108–20.

    Article  PubMed  Google Scholar 

  21. Chu C, Oda M, Kitasaka T, Misawa K, Fujiwara M, Hayashi Y, et al. Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images. Med Image Comput Comput Assist Interv. 2013;16:165–72.

    PubMed  Google Scholar 

  22. Karasawa K, Oda M, Kitasaka T, Misawa K, Fujiwara M, Chu C, et al. Multi-atlas pancreas segmentation: atlas selection based on vessel structure. Med Image Anal. 2017;39:18–28.

    Article  PubMed  Google Scholar 

  23. Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y. Abdominal multi-organ segmentation from CT images using conditional shape location and unsupervised intensity priors. Med Image Anal. 2015;26(1):1–18.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Tong T, Wolz R, Wang Z, Gao Q, Misawa K, Fujiwara M, et al. Discriminative dictionary learning for abdominal multi-organ segmentation. Med Image Anal. 2015;23(1):92–104.

    Article  PubMed  Google Scholar 

  25. Shen J, Baum T, Cordes C, Ott B, Skurk T, Kooijman H, et al. Automatic segmentation of abdominal organs and adipose tissue compartments in water-fat MRI: application to weight-loss in obesity. Eur J Radiol. 2016;85(9):1613–21.

    Article  PubMed  Google Scholar 

  26. Wolz R, Chu C, Misawa K, Fujiwara M, Mori K, Rueckert D. Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans Med Imaging. 2013;32(9):1723–30.

    Article  PubMed  Google Scholar 

  27. Shimizu A, Kimoto T, Kobatake H, Nawano S, Shinozaki K. Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int J Comput Assist Rad. 2010;5(1):85–98.

    Article  Google Scholar 

  28. Hammon M, Cavallaro A, ErdtvDankerl MP, Kirschner, Drechsler K, et al. Model-based pancreas segmentation in portal venous phase contrast-enhanced CT images. J Digit Imaging. 2013;26(6):1082–90.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Erdt M, Kirschner M, Drechsler K, Wesarg S, Hammon M, Cavallaro A (2011) Automatic pancreas segmentation in contrast enhanced CT data using learned spatial anatomy and texture descriptors, in: Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Micro, pp 2076–2082

  30. Saito A, Nawano S, Shimizu A. Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs. Med Image Anal. 2016;28:46–65.

    Article  PubMed  Google Scholar 

  31. Li S, Jiang H, Wang Z, Zhang G, Yao Y. An effective computer aided diagnosis model for pancreas cancer on PET/CT images. Comput Methods Programs Biomed. 2018;165:205–14.

    Article  PubMed  Google Scholar 

  32. Fu M, Wu W, Hong X, Liu Q, Jiang J, Ou Y, et al. Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images. BMC Syst Biol. 2018;12:56.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Boers TGW, Hu Y, Gibson E, Barratt DC, Bonmati E, Krdzalic J, et al. Interactive 3D U-net for the segmentation of the pancreas in computed tomography scans. Phys Med Biol. 2020;65(6):065002.

    Article  CAS  PubMed  Google Scholar 

  34. Bobo MF, Bao S, Huo Y, Yao Y, Virostko J, Plassard AJ, et al. Fully convolutional neural networks improve abdominal organ segmentation. Proc SPIE Int Soc Opt Eng. 2018;10574:105742V.

    PubMed  PubMed Central  Google Scholar 

  35. Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, et al. Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans Med Imaging. 2018;37(8):1822–34.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Yu Q, Xie L, Wang Y, Zhou Y, Fishman EK, Yuille AL (2018) Recurrent saliency transformation network: incorporating multi-stage visual cues for small organ segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8280–8289.

  37. Zhou Y, Xie L, Shen W, Fishman E, Yuille A. Pancreas segmentation in abdominal CT scan: a coarse-to-fine approach. arXiv: 1612.08230 , 2016.

  38. Li H, Reichert M, Lin K, Tselousov N, Braren R, Fu D, et al. Differential diagnosis for pancreatic cysts in CT scans using densely-connected convolutional networks. Conf Proc IEEE Eng Med Biol Soc. 2019;2019:2095–8.

    Google Scholar 

  39. Dmitriev K, Kaufman AE, Javed AA, Hruban RH, Fishman EK, Lennon AM, et al., “Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble”, In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins D, Duchesne S. (eds) Medical Image Computing and Computer Assisted Intervention - MICCAI 2017,. MICCAI 2017. Lecture Notes in Computer Science. New York: Springer Verlag; 2017; 10435:150-158

  40. Watson MD, Lyman WB, Passeri MJ, Murphy KJ, Sarantou JP, Iannitti DA, et al. Use of artificial intelligence deep learning to determine the malignant potential of pancreatic cystic neoplasms with preoperative computed tomography imaging. Am Surg. 2020. https://doi.org/10.1177/0003134820953779.

    Article  PubMed  Google Scholar 

  41. Corral JE, Hussein S, Kandel P, Bolan CW, Bagci U, Wallace MB. Deep learning to classify intraductal papillary mucinous neoplasms using magnetic resonance imaging. Pancreas. 2019;48(6):805–10.

    Article  PubMed  Google Scholar 

  42. Zhu Z, Xia Y, Xie L, Fishman EK, Yuille AL. Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. https://arxiv.org/pdf/1807.02941.pdf2018.

  43. Chu LC, Park S, Kawamoto S, Wang Y, Zhou Y, Shen W, et al. Application of deep learning to pancreatic cancer detection: lessons learned from our initial experience. J Am Coll Radiol. 2019;16(9):1338–42.

    Article  PubMed  Google Scholar 

  44. Liu K-L, Wu T, Chen P-T, Tsai YM, Roth H, Wu M-S, et al. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digital Health. 2020;2:e303–13.

    Article  PubMed  Google Scholar 

  45. Ma H, Liu ZX, Zhang JJ, Wu FT, Xu CF, Shen Z, et al. Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis. World J Gastroenterol. 2020;26(34):5156–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhang Z, Li S, Wang Z, Lu Y. A novel and efficient tumor detection framework for pancreatic cancer via CT images. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:1160–4.

    PubMed  Google Scholar 

  47. Liu SL, Li S, Guo YT, Zhou YP, Zhang ZD, Li S, Lu Y. Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network. Chin Med J. 2019;132(23):2795–803.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Bartoli M, Barat M, Dohan A, Gaujoux S, Coriat R, Hoeffel C, et al. CT and MRI of pancreatic tumors: an update in the era of radiomics. Jpn J Radiol. 2020;38(12):1111–24.

    Article  PubMed  Google Scholar 

  49. Kulali F, Semiz-Oysu A, Demir M, Segmen-Yilmaz M, Bukte Y. Role of diffusion-weighted MR imaging in predicting the grade of nonfunctional pancreatic neuroendocrine tumors. Diagn Interv Imaging. 2018;99(5):301–9.

    Article  CAS  PubMed  Google Scholar 

  50. Wei R, Lin K, Yan W, Guo Y, Wang Y, Zhu J. Computer-aided diagnosis of pancreas serous cystic neoplasms: a radiomics method on preoperative MDCT images. Technol Cancer Res Treat. 2019. https://doi.org/10.1177/1533033818824339.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Takahashi M, Fujinaga Y, Notohara K, Koyama T, Inoue D, Irie H, Gabata T, Kadoya M, Kawa S, Okazaki K, Working Group Members of The Research Program on Intractable Diseases from the Ministry of Labor, Welfare of Japan. Diagnostic imaging guide for autoimmune pancreatitis. Jpn J Radiol. 2020;38(7):591–612.

    Article  PubMed  Google Scholar 

  52. Ziegelmayer S, Kaissis G, Harder F, Jungmann F, Müller T, Makowski M, et al. Deep convolutional neural network-assisted feature extraction for diagnostic discrimination and feature visualization in pancreatic ductal adenocarcinoma versus autoimmune pancreatitis. J Clin Med. 2020;9(12):4013.

    Article  PubMed Central  Google Scholar 

  53. Kaissis G, Ziegelmayer S, Lohöfer F, Algül H, Eiber M, Weichert W, et al. A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging. Eur Radiol Exp. 2019;3(1):41.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Chassagnon G, Dohan A. Artificial intelligence: from challenges to clinical implementation. Diagn Interv Imaging. 2020;101(12):763–4.

    Article  CAS  PubMed  Google Scholar 

  55. National Cancer Institute Clinical Proteomic Tumor Analysis Consortium. Radiology data from the clinical proteomic tumor analysis consortium pancreatic ductal adenocarcinoma (CPTAC-PDA) collection. The Cancer Imaging Archive 2018. https://doi.org/https://doi.org/10.7937/k9/tcia.2018.sc20fo18 (accessed June 15, 2020).

  56. Liao WC, Simpson AL, Wang W. Convolutional neural network for the detection of pancreatic cancer on CT scans—authors’ reply. Lancet Digit Health. 2020;2:e454.

    Article  PubMed  Google Scholar 

  57. Suman G, Panda A, Korfiatis P, Goenka AH. Convolutional neural network for the detection of pancreatic cancer on CT scans. Lancet Digit Health. 2020;2:e453.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, Hopital Cochin, Assistance Publique-Hopitaux de Paris, 27 Rue du Faubourg Saint-Jacques, Paris, France

    Maxime Barat, Guillaume Chassagnon, Anthony Dohan & Philippe Soyer

  2. Université de Paris, Descartes-Paris 5, 75006, Paris, France

    Maxime Barat, Guillaume Chassagnon, Anthony Dohan, Sébastien Gaujoux, Romain Coriat & Philippe Soyer

  3. Department of Abdominal Surgery, Hopital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France

    Sébastien Gaujoux

  4. Department of Gastroenterology, Hopital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France

    Romain Coriat

  5. Department of Radiology, Robert Debré Hospital, 51092, Reims, France

    Christine Hoeffel

  6. Department of Radiology, CHU Montpellier, University of Montpellier, Saint-Éloi Hospital, 34000, Montpellier, France

    Christophe Cassinotto

Authors
  1. Maxime Barat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guillaume Chassagnon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anthony Dohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sébastien Gaujoux
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Romain Coriat
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christine Hoeffel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Christophe Cassinotto
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Philippe Soyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Philippe Soyer.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised due to interchange of Figure 1 and Figure 2 captions.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barat, M., Chassagnon, G., Dohan, A. et al. Artificial intelligence: a critical review of current applications in pancreatic imaging. Jpn J Radiol 39, 514–523 (2021). https://doi.org/10.1007/s11604-021-01098-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11604-021-01098-5

Keywords

Search

Navigation